Elastic Net SearcheR

Description

Search a grid of values of alpha and lambda for minimum mean CV error

Usage

ensr(x, y, alphas = seq(0, 1, length = 10), nlambda = 100L,
  standardize = TRUE, nfolds = 10L, foldid, ...)

Arguments

Lambda Alpha Grid

Description

Construct a data frame with values for lambda and alpha with an indicator to know if the model is worth fitting.

Usage

lambda_alpha_grid(lambdas, alphas, nlambda = 10L, lmin_factor = 1e-04)

Arguments

Examples

data(tbi)
Xmat <- model.matrix( ~ . - injury1 - injury2 - injury3 - 1, data = tbi)
Yvec <- matrix(tbi$injury1, ncol = 1)
alphas <- seq(0, 1, length = 20)

lga <- lambda_alpha_grid(alphas = alphas, lambdas = lambda_max(Yvec, Xmat, alpha = alphas))

ggplot2::ggplot() +
  ggplot2::theme_bw() +
  ggplot2::aes_string(x = "a", y = "log10(l)") +
  ggplot2::geom_path(data = lga$top) +
  ggplot2::geom_point(data = lga$lgrid,
                      mapping = ggplot2::aes(color = cos(a) + sin(log10(l)))) +
  ggplot2::geom_contour(data = lga$lgrid,
                        mapping = ggplot2::aes(z = cos(a) + sin(log10(l)))) +
  ggplot2::scale_color_gradient2(low = "blue", high = "red", mid = "grey")

Lambda Max

Description

Determine the lambda_max value that would be generated from a call to glmnet without making that call.

Usage

lambda_max(y, x, standardize = TRUE, alpha = 0, lmin_factor = 1e-04,
  ...)

Arguments

Examples

data(tbi)
Xmat <- model.matrix( ~ . - injury1 - injury2 - injury3 - 1, data = tbi)
Yvec <- matrix(tbi$injury1, ncol = 1)

alphas <- seq(0, 1, length = 20)
lambda_max(Yvec, Xmat, alpha = alphas)

# Look at different options for standardizing the inputs.

dat <-
  expand.grid(standardize = c(TRUE, FALSE),
              alpha = alphas)

lmax <-
  Map(lambda_max,
      standardize = dat$standardize,
      alpha = dat$alpha,
      MoreArgs = list(y = Yvec, x = Xmat))


gmax <-
  Map(glmnet::glmnet,
      standardize = dat$standardize,
      alpha = dat$alpha,
      MoreArgs = list(y = Yvec, x = Xmat))

dat$gmax <- sapply(gmax, function(f) f$lambda[1])
dat$lmax <- unlist(lmax)

par(mfrow = c(1, 2))

with(subset(dat, standardize == TRUE),
     {
       plot(log10(gmax), log10(lmax))
       abline(0, 1)
       title(main = "standardize == TRUE")
     })

with(subset(dat, standardize == FALSE),
     {
       plot(log10(gmax), log10(lmax))
       abline(0, 1)
       title(main = "standardize == FALSE")
     })

Water Percolation Through A Landfill

Description

A computer simulation of water moving through a landfill. Detailed explanation for the variables and the construction of the data set is found in vignette("ensr-datasets", package = "ensr")

Usage

landfill

Format

An object of class data.table (inherits from data.frame) with 974 rows and 48 columns.

See Also

vignette("ensr-datasets", package = "ensr")

Predict Methods for ensr objects

Description

Using either the lambda.min or lambda.1se, find the preferable model from the ensr object and return a prediction.

Usage

## S3 method for class 'ensr'
predict(object, ...)

## S3 method for class 'ensr'
coef(object, ...)

Arguments

Details

The glmnet::predict argument s is ignored if specified and attempted to be passed via .... The value of s that is passed to glmnet::predict is determined by the value of lambda.min or lambda.1se found from a call to preferable.

Preferable Elastic Net Model

Description

Find the preferable Elastic Net Model from an ensr object. The preferable model is defined as the model with the lowest mean cross validation error and largest alpha value.

Usage

preferable(object, ...)

Arguments

Standardize

Description

Center and scale vectors by mean/standard deviation or median/IQR with the option to base the standardization only on unique observations.

Usage

standardize(x, stats = list(center = "mean", scale = "sd"),
  use_unique = TRUE, margin)

Arguments

Examples

x <- 1:100
standardize(x)
standardize(x, stats = list(center = "median", scale = "IQR"))

xmat <- matrix(1:50, nrow = 10)
standardize(xmat, margin = 0)
standardize(xmat, margin = 1)
standardize(xmat, margin = 2)

xarray <- array(1:60, dim = c(5, 2, 6))
standardize(xarray, margin = 0)
standardize(xarray, margin = 1:2)

# Standardize a data.frame
standardize(mtcars)

# a generic list object
alist <- list(x = rep(1:10, 2), y = rnorm(100), z = matrix(1:10, nrow = 2))
standardize(alist, margin = 0)
standardize(alist, margin = 1)

Synthetic Data Set for Traumatic Brain Injuries

Description

This data is synthetic, that is, it is random data generated to have similar properties to a data set used for studying traumatic brain injuries. The pcode1 ... pcode6, ncode1 ... ncode6 columns are indicators for procedure or billing codes associated with a hospital stay for TBI.

Usage

tbi

Format

a data.table with 1323 rows and 18 columns. Each row of the tbi data.table is a unique subject.

age

age, in days

female

indicator for sex, 1 == female, 0 == male

los

length of stay in the hosptial

pcode1

indicator for if the patient had pcode1

pcode2

indicator for if the patient had pcode2

pcode3

indicator for if the patient had pcode3

pcode4

indicator for if the patient had pcode4

pcode5

indicator for if the patient had pcode5

pcode6

indicator for if the patient had pcode6

ncode1

indicator for if the patient had ncode1

ncode2

indicator for if the patient had ncode2

ncode3

indicator for if the patient had ncode3

ncode4

indicator for if the patient had ncode4

ncode5

indicator for if the patient had ncode5

ncode6

indicator for if the patient had ncode6

injury1

First of three specific types of TBI

injury2

Second of three specific types of TBI

injury3

Third of three specific types of TBI

See Also

vignette("ensr-datasets", package = "ensr")